文献类型：期刊文献

英文题名：Detecting Topic-oriented Overlapping Community Using Hybrid a Hypergraph Model

作者：Shen, G. L.[1];Yang, X. P.[1];Sun, J.[2]

第一作者：Shen, G. L.

通讯作者：Shen, GL[1]

机构：[1]Renmin Univ, Informat Sch, Beijing, Peoples R China;[2]Beijing Union Univ, Sch Business, Beijing, Peoples R China

第一机构：Renmin Univ, Informat Sch, Beijing, Peoples R China

通讯机构：[1]corresponding author), Renmin Univ, Informat Sch, Beijing, Peoples R China.

年份：2016

卷号：11

期号：4

起止页码：538-552

外文期刊名：INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL

收录：;Scopus(收录号：2-s2.0-84981165077);WOS:【SCI-EXPANDED(收录号:WOS:000378943900007)】；

基金：This paper is supported by Natural Science Foundation of China (No. 71572015), Scientific Research Project of Beijing Union University (No. Zk10201506), Beijing Higher Education Young Elite Teacher Project (No. YETP1503).

语种：英文

外文关键词：information network; overlapping community detection; topic-oriented; hybrid hypergraph model

摘要：A large number of emerging information networks brings new challenges to the overlapping community detection. The meaningful community should be topicoriented. However, the topology-based methods only reflect the strength of connection, but ignore the consistency of the topics. This paper explores a topic-oriented overlapping community detection method for information work. The method utilizes a hybrid hypergraph model to combine the node content and structure information naturally. Two connections for hyperedge pair, including real connection and virtual connection are defined. A novel hyperedge pair similarity measure is proposed by combining linearly extended common neighbors metric for real connection and incremental fitness for virtual connection. Extensive experiments on two real-world datasets validate our proposed method outperforms other baseline algorithms.